标签：
softmax回归用来解决K类分类问题，其实就是logistic回归的扩展。
注意：
1.对于sigmod函数g(x)，当x∈[-1,1]时效果比较好。所以先把样本数据进行归一化(本例中就是对每一个数都除以10)
2.这次的参数θ不再是一维的向量了而是二维的矩阵: tt[1..CLS][1..LEN] （CLS表示一共有几类数据，LEN表示每一个样本的维度）。tt[i]*xx[j]表示样本j属于类别i的概率。【从这里很容易看出logistic回归其实就是softmax回归的一种特例啦
3.因为θ变复杂了，所以梯度下降函数也要相应调整。
Code:
1 import numpy as np 2 import csv 3 import math as mt 4 5 def epow(x): 6 ex=mt.e 7 tmp=mt.pow(ex,x) 8 return tmp 9 10 def vmul(aa,bb,x): #[0..x-1] 11 tmp=0.0 12 for i in range(0,x): 13 tmp+=aa[i]*bb[i] 14 return tmp 15 16 def iternum(k,j): 17 tmp=0.1/(k+j+1)+0.1 18 return tmp 19 20 def hypo(x,q): #return h(xx).[q]=(y=q|xx;tt) 21 tx=0.0 22 ty=0.0 23 for j in range(1,CLS+1): #[1..CLS] 24 tmp=vmul(tt[j],x,LEN) 25 ty+=epow(tmp) 26 tx=epow(vmul(tt[q],x,LEN)) 27 tx=tx/ty 28 return tx 29 30 def likeli(t): 31 tmp=0.0 32 for i in range(1,LEN+1): #[1..LEN] 33 tx=1.0 34 for l in range(1,CLS+1): 35 if(yy[i]==l): 36 ty=0.0 37 for j in range(1,CLS+1): 38 ty+=epow(vmul(t[j],xx[i],LEN)) #LEN: length of the vector xx[i] 39 ty=epow(vmul(t[l],xx[i],LEN))/ty 40 tx=tx*ty 41 tx=mt.log(tx) 42 tmp+=tx 43 return tmp 44 45 def GDA(iter): 46 for j in range(1,num+1): #[1..100] 47 for k in range(1,CLS+1): 48 tm=0.0 49 if(yy[j]==k): 50 tm=1.0 51 tm-=hypo(xx[j],k) 52 for i in range(0,LEN): 53 tt[k][i]+=iternum(iter,j)*tm*xx[j][i] 54 55 trainfile=file(‘train.csv‘,‘rb‘) 56 trainread=csv.reader(trainfile) 57 testfile=file(‘test.csv‘,‘rb‘) 58 testread=csv.reader(testfile) 59 LEN=4 #[0..2] 60 CLS=2 #divide the DATA into 2 classes 61 xx=np.zeros((105,LEN+1),float) #(xx,yy):traindata 62 yy=np.zeros(105,float) #[1..num][0..LEN-1,LEN] 63 tt=np.zeros((CLS+1,LEN+1),float) #tt[1..CLS][0..LEN-1,LEN] 64 dx=np.zeros((105,LEN+1),float) #(dx,dy):testdata 65 dy=np.zeros(105,float) #[1..num][0..LEN-1,LEN] 66 67 num=0 68 for line in trainread: 69 num+=1 70 xx[num]=line 71 for i in range(0,LEN): 72 xx[num][i]=xx[num][i]/10 73 yy[num]=xx[num][LEN] 74 trainfile.close() 75 76 dnum=0 77 for line in testread: 78 dnum+=1 79 dx[dnum]=line 80 for i in range(0,LEN): 81 dx[num][i]=dx[num][i]/10 82 dy[dnum]=dx[dnum][LEN] 83 testfile.close() 84 85 for i in range(1,num+1): 86 print(xx[i],yy[i]) 87 print(" ----- ") 88 for i in range(1,dnum+1): 89 print(dx[i],dy[i]) 90 91 iter=0 92 lx=99999.0 93 ly=likeli(tt) 94 while(mt.fabs(ly-lx)>0.001): 95 print(iter,likeli(tt),tt) 96 lx=ly 97 GDA(iter) 98 iter+=1 99 ly=likeli(tt) 100 101 for i in range(1,dnum+1): 102 print("DATA ",i,dy[i]) 103 for j in range(1,CLS+1): 104 tmp=hypo(dx[i],j) 105 print(j," ",tmp)
运行结果：
1 D:\Anaconda\python.exe D:/pycharmprojects/softmax/1.py 2 //下面是训练样本 3 (array([ 0.51, 0.35, 0.14, 0.02, 2. ]), 2.0) 4 (array([ 0.49, 0.3 , 0.14, 0.02, 2. ]), 2.0) 5 (array([ 0.47, 0.32, 0.13, 0.02, 2. ]), 2.0) 6 (array([ 0.46, 0.31, 0.15, 0.02, 2. ]), 2.0) 7 (array([ 0.5 , 0.36, 0.14, 0.02, 2. ]), 2.0) 8 (array([ 0.54, 0.39, 0.17, 0.04, 2. ]), 2.0) 9 (array([ 0.46, 0.34, 0.14, 0.03, 2. ]), 2.0) 10 (array([ 0.5 , 0.34, 0.15, 0.02, 2. ]), 2.0) 11 (array([ 0.44, 0.29, 0.14, 0.02, 2. ]), 2.0) 12 (array([ 0.49, 0.31, 0.15, 0.01, 2. ]), 2.0) 13 (array([ 0.54, 0.37, 0.15, 0.02, 2. ]), 2.0) 14 (array([ 0.48, 0.34, 0.16, 0.02, 2. ]), 2.0) 15 (array([ 0.48, 0.3 , 0.14, 0.01, 2. ]), 2.0) 16 (array([ 0.43, 0.3 , 0.11, 0.01, 2. ]), 2.0) 17 (array([ 0.58, 0.4 , 0.12, 0.02, 2. ]), 2.0) 18 (array([ 0.57, 0.44, 0.15, 0.04, 2. ]), 2.0) 19 (array([ 0.54, 0.39, 0.13, 0.04, 2. ]), 2.0) 20 (array([ 0.51, 0.35, 0.14, 0.03, 2. ]), 2.0) 21 (array([ 0.57, 0.38, 0.17, 0.03, 2. ]), 2.0) 22 (array([ 0.51, 0.38, 0.15, 0.03, 2. ]), 2.0) 23 (array([ 0.54, 0.34, 0.17, 0.02, 2. ]), 2.0) 24 (array([ 0.51, 0.37, 0.15, 0.04, 2. ]), 2.0) 25 (array([ 0.46, 0.36, 0.1 , 0.02, 2. ]), 2.0) 26 (array([ 0.51, 0.33, 0.17, 0.05, 2. ]), 2.0) 27 (array([ 0.48, 0.34, 0.19, 0.02, 2. ]), 2.0) 28 (array([ 0.5 , 0.3 , 0.16, 0.02, 2. ]), 2.0) 29 (array([ 0.5 , 0.34, 0.16, 0.04, 2. ]), 2.0) 30 (array([ 0.52, 0.35, 0.15, 0.02, 2. ]), 2.0) 31 (array([ 0.52, 0.34, 0.14, 0.02, 2. ]), 2.0) 32 (array([ 0.47, 0.32, 0.16, 0.02, 2. ]), 2.0) 33 (array([ 0.48, 0.31, 0.16, 0.02, 2. ]), 2.0) 34 (array([ 0.54, 0.34, 0.15, 0.04, 2. ]), 2.0) 35 (array([ 0.52, 0.41, 0.15, 0.01, 2. ]), 2.0) 36 (array([ 0.55, 0.42, 0.14, 0.02, 2. ]), 2.0) 37 (array([ 0.49, 0.31, 0.15, 0.01, 2. ]), 2.0) 38 (array([ 0.5 , 0.32, 0.12, 0.02, 2. ]), 2.0) 39 (array([ 0.55, 0.35, 0.13, 0.02, 2. ]), 2.0) 40 (array([ 0.49, 0.31, 0.15, 0.01, 2. ]), 2.0) 41 (array([ 0.44, 0.3 , 0.13, 0.02, 2. ]), 2.0) 42 (array([ 0.51, 0.34, 0.15, 0.02, 2. ]), 2.0) 43 (array([ 0.5 , 0.35, 0.13, 0.03, 2. ]), 2.0) 44 (array([ 0.45, 0.23, 0.13, 0.03, 2. ]), 2.0) 45 (array([ 0.44, 0.32, 0.13, 0.02, 2. ]), 2.0) 46 (array([ 0.5 , 0.35, 0.16, 0.06, 2. ]), 2.0) 47 (array([ 0.51, 0.38, 0.19, 0.04, 2. ]), 2.0) 48 (array([ 0.48, 0.3 , 0.14, 0.03, 2. ]), 2.0) 49 (array([ 0.51, 0.38, 0.16, 0.02, 2. ]), 2.0) 50 (array([ 0.46, 0.32, 0.14, 0.02, 2. ]), 2.0) 51 (array([ 0.53, 0.37, 0.15, 0.02, 2. ]), 2.0) 52 (array([ 0.5 , 0.33, 0.14, 0.02, 2. ]), 2.0) 53 (array([ 0.7 , 0.32, 0.47, 0.14, 1. ]), 1.0) 54 (array([ 0.64, 0.32, 0.45, 0.15, 1. ]), 1.0) 55 (array([ 0.69, 0.31, 0.49, 0.15, 1. ]), 1.0) 56 (array([ 0.55, 0.23, 0.4 , 0.13, 1. ]), 1.0) 57 (array([ 0.65, 0.28, 0.46, 0.15, 1. ]), 1.0) 58 (array([ 0.57, 0.28, 0.45, 0.13, 1. ]), 1.0) 59 (array([ 0.63, 0.33, 0.47, 0.16, 1. ]), 1.0) 60 (array([ 0.49, 0.24, 0.33, 0.1 , 1. ]), 1.0) 61 (array([ 0.66, 0.29, 0.46, 0.13, 1. ]), 1.0) 62 (array([ 0.52, 0.27, 0.39, 0.14, 1. ]), 1.0) 63 (array([ 0.5 , 0.2 , 0.35, 0.1 , 1. ]), 1.0) 64 (array([ 0.59, 0.3 , 0.42, 0.15, 1. ]), 1.0) 65 (array([ 0.6 , 0.22, 0.4 , 0.1 , 1. ]), 1.0) 66 (array([ 0.61, 0.29, 0.47, 0.14, 1. ]), 1.0) 67 (array([ 0.56, 0.29, 0.36, 0.13, 1. ]), 1.0) 68 (array([ 0.67, 0.31, 0.44, 0.14, 1. ]), 1.0) 69 (array([ 0.56, 0.3 , 0.45, 0.15, 1. ]), 1.0) 70 (array([ 0.58, 0.27, 0.41, 0.1 , 1. ]), 1.0) 71 (array([ 0.62, 0.22, 0.45, 0.15, 1. ]), 1.0) 72 (array([ 0.56, 0.25, 0.39, 0.11, 1. ]), 1.0) 73 (array([ 0.59, 0.32, 0.48, 0.18, 1. ]), 1.0) 74 (array([ 0.61, 0.28, 0.4 , 0.13, 1. ]), 1.0) 75 (array([ 0.63, 0.25, 0.49, 0.15, 1. ]), 1.0) 76 (array([ 0.61, 0.28, 0.47, 0.12, 1. ]), 1.0) 77 (array([ 0.64, 0.29, 0.43, 0.13, 1. ]), 1.0) 78 (array([ 0.66, 0.3 , 0.44, 0.14, 1. ]), 1.0) 79 (array([ 0.68, 0.28, 0.48, 0.14, 1. ]), 1.0) 80 (array([ 0.67, 0.3 , 0.5 , 0.17, 1. ]), 1.0) 81 (array([ 0.6 , 0.29, 0.45, 0.15, 1. ]), 1.0) 82 (array([ 0.57, 0.26, 0.35, 0.1 , 1. ]), 1.0) 83 (array([ 0.55, 0.24, 0.38, 0.11, 1. ]), 1.0) 84 (array([ 0.55, 0.24, 0.37, 0.1 , 1. ]), 1.0) 85 (array([ 0.58, 0.27, 0.39, 0.12, 1. ]), 1.0) 86 (array([ 0.6 , 0.27, 0.51, 0.16, 1. ]), 1.0) 87 (array([ 0.54, 0.3 , 0.45, 0.15, 1. ]), 1.0) 88 (array([ 0.6 , 0.34, 0.45, 0.16, 1. ]), 1.0) 89 (array([ 0.67, 0.31, 0.47, 0.15, 1. ]), 1.0) 90 (array([ 0.63, 0.23, 0.44, 0.13, 1. ]), 1.0) 91 (array([ 0.56, 0.3 , 0.41, 0.13, 1. ]), 1.0) 92 (array([ 0.55, 0.25, 0.4 , 0.13, 1. ]), 1.0) 93 (array([ 0.55, 0.26, 0.44, 0.12, 1. ]), 1.0) 94 (array([ 0.61, 0.3 , 0.46, 0.14, 1. ]), 1.0) 95 (array([ 0.58, 0.26, 0.4 , 0.12, 1. ]), 1.0) 96 (array([ 0.5 , 0.23, 0.33, 0.1 , 1. ]), 1.0) 97 (array([ 0.56, 0.27, 0.42, 0.13, 1. ]), 1.0) 98 (array([ 0.57, 0.3 , 0.42, 0.12, 1. ]), 1.0) 99 (array([ 0.57, 0.29, 0.42, 0.13, 1. ]), 1.0) 100 (array([ 0.62, 0.29, 0.43, 0.13, 1. ]), 1.0) 101 (array([ 0.51, 0.25, 0.3 , 0.11, 1. ]), 1.0) 102 (array([ 0.57, 0.28, 0.41, 0.13, 1. ]), 1.0) 103 ----- //下面是测试样本 104 (array([ 4.8, 3.4, 1.6, 0.2, 2. ]), 2.0) 105 (array([ 4.8, 3. , 1.4, 0.1, 2. ]), 2.0) 106 (array([ 4.3, 3. , 1.1, 0.1, 2. ]), 2.0) 107 (array([ 5.8, 4. , 1.2, 0.2, 2. ]), 2.0) 108 (array([ 5.6, 2.5, 3.9, 1.1, 1. ]), 1.0) 109 (array([ 5.9, 3.2, 4.8, 1.8, 1. ]), 1.0) 110 (array([ 6.1, 2.8, 4. , 1.3, 1. ]), 1.0) 111 (array([ 6.3, 2.5, 4.9, 1.5, 1. ]), 1.0) 112 (array([ 6.1, 2.8, 4.7, 1.2, 1. ]), 1.0) 113 //格式：(迭代次数，似然函数值，参数tt) 114 (0, -2.772588722239781, array([[ 0., 0., 0., 0., 0.], 115 [ 0., 0., 0., 0., 0.], 116 [ 0., 0., 0., 0., 0.]])) 117 (1, -4.514315589179653, array([[ 0. , 0. , 0. , 0. , 0. ], 118 [ 0.5145982 , 0.05042281, 0.73611002, 0.26706586, 0. ], 119 [-0.50370467, -0.04718208, -0.72477409, -0.26317434, 0. ]])) 120 (2, -4.227215487909576, array([[ 0. , 0. , 0. , 0. , 0. ], 121 [ 0.45433865, -0.23090121, 1.18513854, 0.45716201, 0. ], 122 [-0.43905776, 0.23361395, -1.16573056, -0.45025988, 0. ]])) 123 (3, -3.7663141540304017, array([[ 0. , 0. , 0. , 0. , 0. ], 124 [ 0.34235568, -0.52470169, 1.5748469 , 0.62663847, 0. ], 125 [-0.32372301, 0.52655101, -1.54839518, -0.6170723 , 0. ]])) 126 (4, -3.351323062397454, array([[ 0. , 0. , 0. , 0. , 0. ], 127 [ 0.23350681, -0.79912339, 1.9323152 , 0.782718 , 0. ], 128 [-0.21195882, 0.80012599, -1.89955448, -0.77075573, 0. ]])) 129 (5, -2.9952661316895712, array([[ 0. , 0. , 0. , 0. , 0. ], 130 [ 0.13281698, -1.05286174, 2.26314675, 0.92733284, 0. ], 131 [-0.1086993 , 1.05307502, -2.22474157, -0.91322043, 0. ]])) 132 (6, -2.6911645875438817, array([[ 0. , 0. , 0. , 0. , 0. ], 133 [ 0.04008866, -1.28766414, 2.57038587, 1.06173426, 0. ], 134 [-0.0137102 , 1.28714809, -2.52694564, -1.04569818, 0. ]])) 135 (7, -2.4310033411415093, array([[ 0. , 0. , 0. , 0. , 0. ], 136 [-0.04540335, -1.50544051, 2.85652835, 1.18698672, 0. ], 137 [ 0.07376713, 1.50425253, -2.80860351, -1.16923217, 0. ]])) 138 (8, -2.2076937297751518, array([[ 0. , 0. , 0. , 0. , 0. ], 139 [-0.12438972, -1.70794838, 3.12376352, 1.30403084, 0. ], 140 [ 0.15449676, 1.7061424 , -3.07184422, -1.28474108, 0. ]])) 141 (9, -2.0152221051169965, array([[ 0. , 0. , 0. , 0. , 0. ], 142 [-0.19754228, -1.89676316, 3.37402437, 1.41369945, 0. ], 143 [ 0.22918248, 1.89438939, -3.31854265, -1.39303654, 0. ]])) 144 (10, -1.8485615066553978, array([[ 0. , 0. , 0. , 0. , 0. ], 145 [-0.26546623, -2.07328532, 3.60901584, 1.5167286 , 0. ], 146 [ 0.29845873, 2.07039023, -3.55034999, -1.49483488, 0. ]])) 147 (11, -1.7035420403682222, array([[ 0. , 0. , 0. , 0. , 0. ], 148 [-0.32870183, -2.23875443, 3.83024003, 1.61376783, 0. ], 149 [ 0.36289166, 2.23538082, -3.76872006, -1.59076787, 0. ]])) 150 (12, -1.5767187419565711, array([[ 0. , 0. , 0. , 0. , 0. ], 151 [-0.38772876, -2.39426506, 4.03901997, 1.70538999, 0. ], 152 [ 0.42298345, 2.39045216, -3.9749333 , -1.68139268, 0. ]])) 153 (13, -1.465249925219289, array([[ 0. , 0. , 0. , 0. , 0. ], 154 [-0.44297145, -2.54078266, 4.23652149, 1.79210051, 0. ], 155 [ 0.47917776, 2.53656633, -4.17011849, -1.76720095, 0. ]])) 156 (14, -1.366790841468854, array([[ 0. , 0. , 0. , 0. , 0. ], 157 [-0.49480477, -2.67915874, 4.4237731 , 1.87434569, 0. ], 158 [ 0.53186576, 2.67457169, -4.35527213, -1.84862711, 0. ]])) 159 (15, -1.279403600912318, array([[ 0. , 0. , 0. , 0. , 0. ], 160 [-0.54355954, -2.81014484, 4.60168369, 1.95252024, 0. ], 161 [ 0.581392 , 2.80521684, -4.53127561, -1.92605557, 0. ]])) 162 (16, -1.201482384930097, array([[ 0. , 0. , 0. , 0. , 0. ], 163 [-0.58952775, -2.93440516, 4.77105806, 2.0269739 , 0. ], 164 [ 0.62806003, 2.92916325, -4.69891011, -1.99982726, 0. ]])) 165 (17, -1.1316921449530692, array([[ 0. , 0. , 0. , 0. , 0. ], 166 [-0.63296735, -3.05252766, 4.93261044, 2.09801724, 0. ], 167 [ 0.67213751, 3.04699645, -4.85886965, -2.07024512, 0. ]])) 168 (18, -1.0689187527177835, array([[ 0. , 0. , 0. , 0. , 0. ], 169 [-0.67410646, -3.16503394, 5.08697632, 2.1659267 , 0. ], 170 [ 0.71386074, 3.15923578, -5.01177236, -2.13757905, 0. ]])) 171 (19, -1.0122286447337796, array([[ 0. , 0. , 0. , 0. , 0. ], 172 [-0.71314722, -3.27238775, 5.23472253, 2.23094896, 0. ], 173 [ 0.75343874, 3.26634296, -5.15817015, -2.2020701 , 0. ]])) 174 (20, -0.9608362196301394, array([[ 0. , 0. , 0. , 0. , 0. ], 175 [-0.75026908, -3.37500243, 5.37635605, 2.29330469, 0. ], 176 [ 0.79105679, 3.36872949, -5.29855715, -2.26393407, 0. ]])) 177 (21, -0.9140775062980213, array([[ 0. , 0. , 0. , 0. , 0. ], 178 [-0.78563169, -3.4732473 , 5.51233148, 2.35319186, 0. ], 179 [ 0.82687947, 3.46676303, -5.43337686, -2.3233647 , 0. ]])) 180 (22, -0.8713888752894932, array([[ 0. , 0. , 0. , 0. , 0. ], 181 [-0.81937742, -3.56745317, 5.64305758, 2.41078849, 0. ], 182 [ 0.86105337, 3.56077293, -5.56302845, -2.38053633, 0. ]])) 183 (23, -0.8322897941423284, array([[ 0. , 0. , 0. , 0. , 0. ], 184 [-0.85163352, -3.65791719, 5.76890282, 2.46625507, 0. ], 185 [ 0.89370933, 3.65105496, -5.68787203, -2.43560628, 0. ]])) 186 (24, -0.7963688219230861, array([[ 0. , 0. , 0. , 0. , 0. ], 187 [-0.88251403, -3.74490689, 5.89020025, 2.51973669, 0. ], 188 [ 0.92496448, 3.73787547, -5.80823335, -2.48871686, 0. ]])) 189 (25, 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array([[ 0. , 0. , 0. , 0. , 0. ], 502 [ -2.05197651, -7.29085376, 11.02213164, 4.80819831, 0. ], 503 [ 2.10579052, 7.28136378, -10.91703404, -4.7679361 , 0. ]])) 504 (130, -0.1387749005374439, array([[ 0. , 0. , 0. , 0. , 0. ], 505 [ -2.05710679, -7.30727354, 11.04651178, 4.81922768, 0. ], 506 [ 2.11096661, 7.2977881 , -10.94134965, -4.77893974, 0. ]])) 507 (131, -0.13771051590188077, array([[ 0. , 0. , 0. , 0. , 0. ], 508 [ -2.0621963 , -7.32356814, 11.0707099 , 4.8301765 , 0. ], 509 [ 2.11610158, 7.31408732, -10.96548397, -4.78986313, 0. ]])) 510 (132, -0.13666257063799675, array([[ 0. , 0. , 0. , 0. , 0. ], 511 [ -2.06724572, -7.33973946, 11.09472873, 4.84104597, 0. ], 512 [ 2.12119608, 7.33026332, -10.98943969, -4.80070746, 0. ]])) 513 (133, -0.13563068581305257, array([[ 0. , 0. , 0. , 0. , 0. ], 514 [ -2.07225565, -7.35578938, 11.11857093, 4.85183728, 0. ], 515 [ 2.12625075, 7.34631798, -11.01321949, -4.8114739 , 0. ]])) 516 (134, -0.13461449404497236, array([[ 0. , 0. , 0. , 0. , 0. ], 517 [ -2.07722672, -7.37171972, 11.14223911, 4.86255155, 0. ], 518 [ 2.1312662 , 7.36225311, -11.03682594, -4.82216358, 0. ]])) 519 (135, -0.13361363906627216, array([[ 0. , 0. , 0. , 0. , 0. ], 520 [ -2.08215952, -7.38753225, 11.16573583, 4.87318991, 0. ], 521 [ 2.13624305, 7.37807049, -11.0602616 , -4.83277761, 0. ]])) 522 //下面是样本分类结果： 523 //(DATA，编号，原类别) 524 //(i，属于类别i的概率) 525 //...... 526 (‘DATA ‘, 1, 2.0) 527 (1, ‘ ‘, 4.549426925361446e-15) 528 (2, ‘ ‘, 0.9999999999999954) 529 (‘DATA ‘, 2, 2.0) 530 (1, ‘ ‘, 7.437806286527796e-15) 531 (2, ‘ ‘, 0.9999999999999926) 532 (‘DATA ‘, 3, 2.0) 533 (1, ‘ ‘, 7.722102243838512e-17) 534 (2, ‘ ‘, 0.9999999999999999) 535 (‘DATA ‘, 4, 2.0) 536 (1, ‘ ‘, 1.249562190027437e-24) 537 (2, ‘ ‘, 1.0) 538 (‘DATA ‘, 5, 1.0) 539 (1, ‘ ‘, 1.0) 540 (2, ‘ ‘, 9.503727473427473e-17) 541 (‘DATA ‘, 6, 1.0) 542 (1, ‘ ‘, 1.0) 543 (2, ‘ ‘, 2.3147075539380976e-23) 544 (‘DATA ‘, 7, 1.0) 545 (1, ‘ ‘, 0.999999999999999) 546 (2, ‘ ‘, 1.027428387144679e-15) 547 (‘DATA ‘, 8, 1.0) 548 (1, ‘ ‘, 1.0) 549 (2, ‘ ‘, 7.955629841599608e-27) 550 (‘DATA ‘, 9, 1.0) 551 (1, ‘ ‘, 1.0) 552 (2, ‘ ‘, 4.604559079838839e-22) 553 554 Process finished with exit code 0
测试时使用的iris数据集的前两类
Reference:
http://www.cnblogs.com/neopenx/p/4316611.html
http://ufldl.stanford.edu/wiki/index.php/Softmax%E5%9B%9E%E5%BD%92
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原文地址：http://www.cnblogs.com/pdev/p/4589952.html
